The Visual Computer

, Volume 35, Issue 5, pp 753–776 | Cite as

Convolutional neural networks for crowd behaviour analysis: a survey

  • Gaurav Tripathi
  • Kuldeep Singh
  • Dinesh Kumar VishwakarmaEmail author


Interest in automatic crowd behaviour analysis has grown considerably in the last few years. Crowd behaviour analysis has become an integral part all over the world for ensuring peaceful event organizations and minimum casualties in the places of public and religious interests. Traditionally, the area of crowd analysis was computed using handcrafted features. However, the real-world images and videos consist of nonlinearity that must be used efficiently for gaining accuracies in the results. As in many other computer vision areas, deep learning-based methods have taken giant strides for obtaining state-of-the-art performance in crowd behaviour analysis. This paper presents a comprehensive survey of current convolution neural network (CNN)-based methods for crowd behaviour analysis. We have also surveyed popular software tools for CNN in the recent years. This survey presents detailed attributes of CNN with special emphasis on optimization methods that have been utilized in CNN-based methods. It also reviews fundamental and innovative methodologies, both conventional and latest methods of CNN, reported in the last few years. We introduce a taxonomy that summarizes important aspects of the CNN for approaching crowd behaviour analysis. Details of the proposed architectures, crowd analysis needs and their respective datasets are reviewed. In addition, we summarize and discuss the main works proposed so far with particular interest on CNNs on how they treat the temporal dimension of data, their highlighting features and opportunities and challenges for future research. To the best of our knowledge, this is a unique survey for crowd behaviour analysis using the CNN. We hope that this survey would become a reference in this ever-evolving field of research.


Convolutional neural networks Crowd behaviour Stochastic gradient descent Deep learning Anomaly detection 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Gaurav Tripathi
    • 1
  • Kuldeep Singh
    • 1
  • Dinesh Kumar Vishwakarma
    • 2
    Email author
  1. 1.Central Research LabBharat Electronics Ltd.GhaziabadIndia
  2. 2.Department of Information TechnologyDelhi Technological UniversityDelhiIndia

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